Tracking the Intangible: Quantifying Effort in NFL Running Backs
Image source: The Tower
Introduction
Describe the problem and why it is important.
American Football is one of the most-watched and popular sports in the U.S., known for its quick decision-making, complex tactics, and athletically demanding displays of strength, endurance and speed.
Data
Describe the data you’re using in detail, where you accessed it, along with relevant exploratory data analysis (EDA). You should also include descriptions of any relevant data pre-processing steps (e.g., whether you consider specific observations, create any meaningful features, etc.—but don’t mention minor steps like column type conversion, filtering out unnecessary rows)
The data used for this project were from NFL Big Data Bowl 2022 Dataset (NFL Big Data Bowl 2022) on Kaggle.
We limited our dataset to NFL running backs with more than 20 rushes.
Methods
Describe the modeling techniques you chose, their assumptions, justifications for why they are appropriate for the problem, and how you’re comparing/evaluating the different methods.
Used Dr. Ron Yurko and Quang Nguyen’s code to calculate distance from the nearest defender (Nguyen 2023)
Based our AS/ AKE curved on the article titled “Individual acceleration-speed profile in-situ: A proof of concept in professional football players”(Morin et al. 2021)
Still using the non-linear quantile regression plot? (Ding 2024)
Results
Describe your results. This can include tables and plots showing your results, as well as text describing how your models worked and the appropriate interpretations of the relevant output. (Note: Don’t just write out the textbook interpretations of all model coefficients. Instead, interpret the output that is relevant for your question of interest that is framed in the introduction)
Discussion
Give your conclusions and summarize what you have learned with regards to your question of interest. Are there any limitations with the approaches you used? What do you think are the next steps to follow-up your project?
Appendix
Non-linear quantile regression for acceleration vs speed
Metrics
Effort metric v2
Tables
| Running Back | Distance Score |
|---|---|
| Chase Edmonds | 0.18 |
| Rachaad White | 0.18 |
| Alexander Mattison | 0.17 |
| Najee Harris | 0.17 |
| Matt Breida | 0.17 |
| Melvin Gordon | 0.17 |
| James Cook | 0.17 |
| D’Andre Swift | 0.17 |
| Joshua Kelley | 0.17 |
| Latavius Murray | 0.17 |
| Running Back | Distance Score |
|---|---|
| Breece Hall | 0.14 |
| Brian Robinson | 0.14 |
| David Montgomery | 0.14 |
| Justice Hill | 0.14 |
| Tyler Allgeier | 0.14 |
| Darrell Henderson | 0.14 |
| Christian McCaffrey | 0.14 |
| Gus Edwards | 0.13 |
| Isiah Pacheco | 0.13 |
| Deon Jackson | 0.13 |
- Percentage of total points that lie in between the percentile P_{99} and P_{99}-3
- This effort metric quantifies how often a player comes close to his “best” (99th percentile) accelerations
Trying a different interactive layout
References
Ding, Peng. 2024. Linear Models and Extensions. Chapman & Hall. https://arxiv.org/pdf/2401.00649.
Morin, Jean-Benoit, Yann Le Mat, Cristian Osgnach, Andrea Barnabò, Alessandro Pilati, Pierre Samozino, and Pietro E. di Prampero. 2021. “Individual Acceleration-Speed Profile in-Situ: A Proof of Concept in Professional Football Players.” Journal of Biomechanics 123: 110524. https://doi.org/https://doi.org/10.1016/j.jbiomech.2021.110524.
NFL Big Data Bowl. 2022. “NFL Big Data Bowl 2022 Dataset.” Kaggle. https://www.kaggle.com/competitions/nfl-big-data-bowl-2022/data.
Nguyen, Quang. 2023. “Turn-Angle.” https://github.com/qntkhvn/turn-angle/blob/main/scripts/01a_prep_rusher_data.R.